This paper analyzes quantiles of heavy-tailed distributions, separating projection direction and quantile threshold effects.
problem Analyzing quantiles of heavy-tailed distributions with estimated parameters.
method Introduces a Q-Q orthogonality formulation to separate projection-direction and quantile-threshold effects.
result Decomposes the difference between empirical and population quantiles into three terms.
Quantile TD learning outperforms classical TD learning for value estimation.
problem Temporal-difference learning in reinforcement learning.
method Quantile Temporal-Difference Learning (QTD) for policy evaluation.
result QTD offers superior performance to classical TD learning, even in tabular settings.
Investigates methods to regularize quantile regression for accurate predictions.
problem Improving accuracy and fairness in quantile regression predictions.
method Various regularization techniques including expected pinball loss, monotonicity constraints, and rate constraints.
result Deep lattice networks can maintain non-crossing quantiles and improve calibration and fairness.
Paper finds robust Λ-quantiles equal to extremal distributions.
problem Investigating robust models for Λ-quantiles with partial loss information. method Extending classical quantiles using Λ-quantiles and applying results from robust quantiles. result Robust Λ-quantiles equal to Λ-quantiles of extremal distributions. SPQR package uses neural networks for flexible quantile regression.
problem Flexible modeling of non-linear relationships in quantile regression.
method Monotonic splines and neural networks for density estimation; model-agnostic covariate effects.
result Allows for non-linear and quantile-specific effects.
A new method avoids quantile crossing in time series forecasting.
problem Quantile crossing in joint quantile regressions.
method Incremental (Spline) Quantile Functions (I(S)QF) with neural network.
result Improves consistency and accuracy in time series forecasting.
New framework forecasts ES using weighted quantiles.
problem Forecasting Expected Shortfall (ES) in financial markets.
method Two-step procedure: VaR estimation through quantile regressions, ES computation as weighted average.
result Proposed models outperform other methods in stock market indices forecasting.
Quantile Temporal-Difference learning proved convergent with proof.
problem Lack of theoretical understanding of QTD despite empirical success.
method Proof of convergence using stochastic approximation and non-smooth analysis.
result QTD converges to fixed points with probability 1.
Private estimation of many quantiles using differential privacy.
problem Estimating quantiles of a distribution privately.
method Two approaches: 1) Private estimation of empirical quantiles, 2) Uniform density estimation.
result There is a tradeoff between estimating quantiles at specific points and uniformly estimating the quantile function.
Constructs bivariate quantiles using vine copulas for multivariate analysis.
problem Need for research in multivariate quantiles, especially for bivariate responses.
method Constructs bivariate (conditional) quantiles using vine copula based bivariate regression model with a novel tree sequence graph structure.
result Avoids typical shortfalls of regression like transformations, interactions, collinearity, and quantile crossings.
New method combines CATE and CQTE to estimate treatment effects across different quantiles.
problem Challenges in estimating CQTE due to its dependence on smoothness of individual quantiles.
method Introduces a new estimand, the conditional quantile comparator (CQC), which retains information about the whole treatment distribution and leverages simplicity.
result Demonstrates improved accuracy in estimating treatment effects across different quantiles compared to existing methods.
The paper introduces a new method for forecasting financial risk using quantile-based modeling.
problem Forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) for financial returns.
method Semiparametric approach using restricted quantile regression to model the conditional scale of financial returns.
result The method provides robust, distribution-free estimates of extreme losses and captures risk dynamics.
TQF models multivariate uncertainty by learning conditional quantiles.
problem Challenges in fully nonparametric estimation of multivariate conditional distributions.
method Tomographic Quantile Forests (TQF) learns conditional quantiles of directional projections.
result TQF reconstructs multivariate conditional distribution efficiently without convexity restrictions.
We conduct an empirical study using the quantile-based correlation function to uncover the temporal dependencies in financial time series. The study uses intraday data for the S\&P 500 stocks from the New York Stock Exchange. After establishing an empirical overview we compare the quantile-based correlation function to…
Deep learning models forecast multiple yield curves with improved accuracy.
problem Globalization of financial markets affects yield curves.
method Combines self-attention mechanism and nonparametric quantile regression.
result Effective point and interval forecasts of future yields.
We showcase how Quantile Regression (QR) can be applied to forecast financial returns using Limit Order Books (LOBs), the canonical data source of high-frequency financial time-series. We develop a deep learning architecture that simultaneously models the return quantiles for both buy and sell positions. We test our mo…
Value-at-Risk (VaR) is an institutional measure of risk favored by financial regulators. VaR may be interpreted as a quantile of future portfolio values conditional on the information available, where the most common quantile used is 95%. Here we demonstrate Conditional Autoregressive Value at Risk, first introduced by…
Time series quantile regression using GRF for more accurate volatility estimation.
problem Estimating conditional quantiles for time series data accurately.
method Generalized Random Forests (GRF) for quantile regression on time series data.
result The tsQRF estimator is consistent under time series data assumptions.
We develop a novel approach for the construction of quantile processes governing the stochastic dynamics of quantiles in continuous time. Two classes of quantile diffusions are identified: the first, which we largely focus on, features a dynamic random quantile level and allows for direct interpretation of the resultin…
QBVAR improves oil price forecasting across quantiles, especially for downside risk.
problem Forecasting oil prices across different quantiles for better risk assessment.
method Quantile Bayesian Vector Autoregression (QBVAR) model.
result QBVAR improves median forecasts by 2-5% and left-tail forecast improvements of 10-25% during crisis episodes.
Paper converts quantiles to cumulative distribution functions to simplify risk measures.
problem Technical assumptions in risk measure calculations.
method Invention of converting integrated quantiles to integrated cumulative distribution functions.
result Avoids the need for probability density function existence.
Causal inference using observational data is challenging, especially in the bivariate case. Through the minimum description length principle, we link the postulate of independence between the generating mechanisms of the cause and of the effect given the cause to quantile regression. Based on this theory, we develop Bi…
Proposes QQE for transforming and embedding data distributions.
problem Transforming and embedding data distributions for better representation or visualization.
method Quantile-Quantile Embedding (QQE) using quantile-quantile plot concept.
result QQE allows for better discrimination of classes in some cases.
Quantile regression is a tool for learning conditional distributions. In this paper we study quantile regression in the setting where a protected attribute is unavailable when fitting the model. This can lead to "unfair'' quantile estimators for which the effective quantiles are very different for the subpopulations de…
Quantum algorithm samples from SDEs using DQCs and quantile mechanics.
problem Sampling from solutions of stochastic differential equations.
method Differentiable quantum circuits (DQCs) encoding latent variables, quantile mechanics.
result Quantum algorithm generates time-series from SDEs.
Combination of distributional regression algorithms improves uncertainty estimation of satellite precipitation products.
problem Uncertainty estimation in satellite precipitation products.
method Ensemble learning methods combining conditional zero-adjusted probability distributions estimated with GAMLSS, spline-based GAMLSS, and distributional regression forests.
result Stacking of methods outperformed individual methods in most quantile levels using the quantile loss function.
Proposes a non-crossing deep neural network quantile regression method.
problem Quantile crossing in nonparametric quantile regression.
method Non-crossing constraints via rectified linear unit penalty function.
result Established non-asymptotic upper bounds for excess risk.
Nonlinear dynamic volatility has been observed in many financial time series. The recently proposed quantile periodogram offers an alternative way to examine this phenomena in the frequency domain. The quantile periodogram is constructed from trigonometric quantile regression of time series data at different frequencie…
In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases. However, the predictions are neither robust nor adequate enough since they only capture a few conditional distributions instead of the whole distribution, especially for small datasets. …
This paper develops a new method to model treatment effects that are heterogeneous across different quantiles.
problem Modeling treatment effects that vary across different quantiles of the outcome distribution.
method The paper combines quantile classification with local polynomial estimation to build a decision tree and forest.
result The proposed QLPRT and QLPRF methods provide a new way to estimate and infer heterogeneous treatment effects.
We introduce and compare new variability measures based on risk quantiles.
problem Comparing variability measures in risk management.
method Developed a framework for one-parameter families of inter-Expected Shortfall differences and inter-expectile differences.
result Characterized symmetric and comonotonic variability measures as mixtures of inter-Expected Shortfall differences.
Develops new algorithms for QRF to handle mixed-frequency and longitudinal data.
problem Handling mixed-frequency and longitudinal data in quantile regression.
method Mixed-Frequency Quantile Regression Forest (MIDAS-QRF) and Finite Mixture Quantile Regression Forest (FM-QRF).
result Valid and flexible models for complex empirical settings in financial risk management and climate-change impact evaluation.
Bayesian QFSTS model tackles feature selection in quantile time series analysis.
problem Quantile feature selection in correlated multivariate time series data.
method Bayesian dimension reduction methodology using QFSTS model with multivariate asymmetric Laplace distribution, spike-and-slab prior, Metropolis-Hastings algorithm, and Bayesian model averaging.
result QFSTS model outperforms in feature selection, parameter estimation, and forecasting.
Deep model tackles claim size modeling with quantile-based regression.
problem Actuarial claim size modeling difficulty with no simple distribution.
method Deep composite regression model with quantile splicing point.
result Deep neural network regression models show superiority over classical approaches.
A new method for modeling insurance claim frequencies using random proportions.
problem Inaccurate fitting of classical distributions to insurance claim frequency data.
method Modeling claim frequencies using random proportions of insurance contracts and applying goodness-of-fit tests.
result A new statistical approach for better modeling insurance claim frequencies.
Improves quantile regression models by aggregating multiple models.
problem Quantifying uncertainty and modeling diverse populations in predictions.
method Flexible model aggregation using weighted ensembles and modern deep learning.
result Improves accuracy and robustness of quantile predictions.
Distributional Reinforcement Learning (RL) differs from traditional RL in that, rather than the expectation of total returns, it estimates distributions and has achieved state-of-the-art performance on Atari Games. The key challenge in practical distributional RL algorithms lies in how to parameterize estimated distrib…
New method for robustly estimating treatment effects across different risk levels.
problem Missing risks and tail events in CATE, especially in aggregate analyses.
method Constructing a pseudo-outcome and regressing it on covariates using any regression learner.
result Robust and model-agnostic learning of conditional distributional treatment effects (CDTE).
Bayesian optimization (BO) is a popular methodology to tune the hyperparameters of expensive black-box functions. Traditionally, BO focuses on a single task at a time and is not designed to leverage information from related functions, such as tuning performance objectives of the same algorithm across multiple datasets.…
The paper addresses risk sharing and variability measures among agents with general risk preferences.
problem Risk sharing and variability measures among agents with general risk preferences.
method Characterizes Pareto-optimal allocations using Gini deviation, mean-median deviation, and inter-quantile difference as variability measures.
result Optimal allocations are not comonotonic and feature a mixture of pairwise counter-monotonic structures.
It is well known that quantile regression model minimizes the portfolio extreme risk, whenever the attention is placed on the estimation of the response variable left quantiles. We show that, by considering the entire conditional distribution of the dependent variable, it is possible to optimize different risk and perf…
Study improves carbon price forecasting using quantile regression and feature selection.
problem Accurately predicting carbon prices influenced by geopolitical, social, and economic factors.
method Collect and analyze various influencing factors, select significant features, and use Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust predictions.
result Proposed methods outperform existing ones and provide a complete profile of future carbon prices.
We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different approach to generative modeling than those commonly used, that implicitly captures the distribution using quantile regression. AIQN is able to achieve superior perceptual quality and improvements in evaluation metrics, without incu…
Model predicts US COVID-19 deaths with quantile estimates.
problem Predicting US COVID-19 deaths at county level.
method Hybrid machine learning and epidemiological approach, minimizing pinball loss.
result Quantile estimates accurately forecast deaths for different forecast periods.
A new method for optimizing hyperparameters using conformalized quantile regression.
problem Optimizing hyperparameters with strong assumptions about noise.
method Conformalized quantile regression for more realistic modeling.
result Quicker convergence on empirical benchmarks.
condLSTM-Q predicts COVID-19 deaths at county level with quantile forecasts.
problem Predicting COVID-19 mortality at fine geographical scales.
method Conditional Long Short-Term Memory networks with quantile output.
result Fine-scale quantile predictions inform about death toll distribution.
Many investment models in discrete or continuous-time settings boil down to maximizing an objective of the quantile function of the decision variable. This quantile optimization problem is known as the quantile formulation of the original investment problem. Under certain monotonicity assumptions, several schemes to so…
We extend the analysis of investment strategies derived from penalized quantile regression models, introducing alternative approaches to improve state\textendash of\textendash art asset allocation rules. First, we use a post\textendash penalization procedure to deal with overshrinking and concentration issues. Second, …